JP7072803B2 - How to determine the risk of developing open-angle glaucoma in a broad sense - Google Patents

Description

The present invention relates to a method for determining the risk of developing open-angle glaucoma in a broad sense, a device for determining the risk of developing open-angle glaucoma in a broad sense, and a computer program for causing the device to execute.

Glaucoma is a neurodegenerative disease in which retinal ganglion cells are damaged and progress irreversibly, leading to blindness. In addition, it is the number one cause of premature blindness in Japan, and the prevalence of glaucoma over 40 years old is the main type of glaucoma in the broad sense (broad definition primary open-angle glaucoma, broad sense POAG; And normal glaucoma, Nikkei Journal Vol. 116, No. 1, pp. 15-18), which is 3.9%, but most of them are potential glaucoma patients without subjective symptoms. Since the progression of glaucoma can be suppressed by eye drop treatment in the early stage of onset, if the onset can be predicted by screening tests, etc., it is possible to maintain visual function for the rest of the life. It has been.

For example, in Patent Document 1, a known polymorphic site existing on the genome (autosomal chromosome) of a glaucoma patient and a non-patient who has no family history of glaucoma, and in Patent Document 2, a glaucoma patient with fast progression and a slow progression. By comprehensively analyzing each known polymorphism site existing on the patient's genome, single nucleotide polymorphisms (SNPs) associated with the onset / progression of glaucoma can be found, and multiple combinations thereof can be used for determination. Discloses a method for determining whether or not a sample provider is likely to develop glaucoma and whether or not the sample provider is likely to develop glaucoma with higher accuracy.

WO2008 / 130008 Gazette WO2008 / 130009

However, in the methods described in Patent Documents 1 and 2, the judgment accuracy (sensitivity and specificity) is about 60 to 70% at the maximum, and the sensitivity, specificity, and positive predictive value (PPV) are more accurate. , And further improved techniques that can provide a negative predictive value (NPV) are sought.

An object of the present invention is to provide a method for determining the risk of developing POAG in a broad sense with high accuracy, a device for determining the risk of developing POAG in a broad sense for executing the method, and a computer program for causing the device to execute the method.

As a result of diligent studies to solve the above problems, the present inventors have a genome based on genotype information acquired by a high-density chip using a large number of samples of glaucoma patients and healthy non-glaucoma patients possessed by the present inventors. By conducting a genome-wide association study (GWAS), we identified a group of SNPs that are risk markers for the onset of POAG in a broad sense, and among them, we found a specific SNP group that contributes to highly accurate determination, and the SNP group. To complete the present invention, we have found that the risk of developing POAG in a broad sense can be determined with high accuracy by measuring the total number of risk alleles in the sample for the SNP population that combines the SNP group selected from the remaining SNPs and the remaining SNPs. I arrived.

That is, the present invention relates to the following [1] to [3].
[1] A core SNP group consisting of 12 SNPs shown in Table 1 and a pooled SNP group shown in Table 2 based on single nucleotide polymorphism (SNP) allele information in a biological sample collected from a subject. Allele measurement process for measuring alleles for at least 30 SNPs including SNPs selected from (pool selection SNP group),
Based on the measurement results of the allele, the information acquisition process for acquiring information on the risk of developing open-angle glaucoma in the broad-sense subject, and based on the information obtained above, the broad-sense nuclear power plant of the subject. A method of assisting a subject in diagnosing the risk of developing open-angle glaucoma in a broad sense, including an information providing step that provides information for determining the risk of developing open-angle glaucoma.
[2] A computer including a processor and a memory under the control of the processor is provided, and the memory includes a processor.
Selected from the core SNP group consisting of 12 SNPs shown in Table 1 and the pooled SNP group shown in Table 2 based on the allelic information of single nucleotide polymorphisms (SNPs) in biological samples collected from subjects. Allele measurement process for measuring alleles for at least 30 SNPs including SNPs (pool selection SNP group),
Based on the measurement results of the allele, the information acquisition process for acquiring information on the risk of developing open-angle glaucoma in the broad-sense subject, and based on the information obtained above, the broad-sense nuclear power plant of the subject. A subject at risk of developing open-angle glaucoma in a broad sense, in which a computer program for causing the computer to execute an information providing process that provides information for determining the risk of developing open-angle glaucoma is recorded. Detection device.
[3] A computer program including a processor and a memory under the control of the processor.
Selected from the core SNP group consisting of 12 SNPs shown in Table 1 and the pooled SNP group shown in Table 2 based on the allelic information of single nucleotide polymorphisms (SNPs) in biological samples collected from subjects. Allele measurement process for measuring alleles for at least 30 SNPs including SNPs (pool selection SNP group),
Based on the measurement results of the allele, the information acquisition step of acquiring information on the risk of developing open-angle glaucoma in the broad sense of the subject, and based on the information obtained in the above, the broad sense of the subject A computer program that runs an information-providing process that provides information to determine the risk of developing open-angle glaucoma.

By analyzing the SNP group of the present invention present in the sample by the method of the present invention, it is possible to determine the presence or absence of the risk of developing POAG in a broad sense in the sample provider, and further predict the level of the risk. Based on this risk, sample providers can take precautionary measures or receive appropriate treatment for POAG in a broad sense.

FIG. 1 is a schematic view showing an example of a risk determination device for the onset of POAG in a broad sense in a subject. FIG. 2 is a block diagram showing a hardware configuration of the determination device shown in FIG. FIG. 3 is a flowchart for determining the risk of developing POAG in a broad sense using the determination device shown in FIG. FIG. 4 is a diagram showing the results of determining the presence or absence of the onset risk when the number of SNPs to be measured is 30. The figure on the left is a diagram when the top 30 SNPs are used in the GWAS test, and the figure on the right is a diagram when a total of 30 SNPs shown in Table 1 and SNPs selected from Table 2 are used. FIG. 5 shows an example of a graph used for determining the presence or absence of onset risk when the number of SNPs is 90 and the measurement step is one, targeting the SNPs shown in Table 1 and the SNPs selected from Table 2. It is a figure. FIG. 6 is a graph used when individually determining the presence or absence of the onset risk when the number of SNPs is 90 and the measurement step is 3 times, using the SNPs shown in Table 1 and the SNPs selected from Table 2 as measurement targets. It is a figure which showed an example. FIG. 7 is a graph used for integrated determination of the presence or absence of onset risk when the number of SNPs is 90 and the measurement step is 3 times, with the SNPs shown in Table 1 and the SNPs selected from Table 2 as measurement targets. It is a figure which showed an example. FIG. 8 shows the results of using Bayes' theorem to determine the presence or absence of the onset risk when the number of SNPs is 90 and the measurement step is 3 times, using the SNPs shown in Table 1 and the SNPs selected from Table 2 as measurement targets. It is a figure which showed an example. FIG. 9 shows an example of a graph used to determine the presence or absence of onset risk when the number of SNPs is 120 and the measurement step is one, targeting the SNPs shown in Table 1 and the SNPs selected from Table 2. It is a figure. FIG. 10 is a graph used when individually determining the presence or absence of the onset risk when the number of SNPs is 120 and the measurement step is 3 times, using the SNPs shown in Table 1 and the SNPs selected from Table 2 as measurement targets. It is a figure which showed an example. FIG. 11 is a graph used when the presence or absence of the onset risk when the number of SNPs is 120 and the measurement step is 3 times is determined by integrating the SNPs shown in Table 1 and the SNPs selected from Table 2 as measurement targets. It is a figure which showed an example. FIG. 12 shows the results of using Bayes' theorem to determine the presence or absence of the onset risk when the SNPs shown in Table 1 and the SNPs selected from Table 2 are measured, the number of SNPs is 120, and the measurement step is 3 times. It is a figure which showed an example. FIG. 13 shows an example of a graph in which the SNPs shown in Table 1 and the SNPs selected from Table 2 are measured, and the presence or absence of the onset risk is determined when the number of SNPs is 150 and the measurement step is one. It is a figure. FIG. 14 is a graph used when individually determining the presence or absence of the onset risk when the number of SNPs is 150 and the measurement step is 3 times, using the SNPs shown in Table 1 and the SNPs selected from Table 2 as measurement targets. It is a figure which showed an example. FIG. 15 is a graph used when the presence or absence of the onset risk when the number of SNPs is 150 and the measurement step is 3 times is determined by integrating the SNPs shown in Table 1 and the SNPs selected from Table 2 as measurement targets. It is a figure which showed an example. FIG. 16 shows the results of using Bayes' theorem to determine the presence or absence of the onset risk when the SNPs shown in Table 1 and the SNPs selected from Table 2 are measured, the number of SNPs is 150, and the measurement step is 3 times. It is a figure which showed an example.

The present invention may be referred to as broad-sense primary open-angle glaucoma (hereinafter, broadly-defined POAG), which comprises a step of detecting an allele in vitro for a specific single nucleotide polymorphism (hereinafter, also referred to as SNP). ) Is a method for determining the risk of developing POAG in a broad sense by measuring the above-mentioned allele in a sample derived from a subject and using the information when the allele is a risk allele. It is a method that provides information and assists in diagnosing the risk of developing POAG in a broad sense. That is, in the present invention, a measurement target including a specific SNP group that contributes to highly accurate risk determination is set as a member, the total number of risk alleles detected there is counted, and the total number of the risk alleles is a preset threshold value. It has a great feature in that it can provide information that there is a risk of developing POAG in a broad sense when it exceeds. This makes it possible to assist in diagnosing the risk of developing POAG in a broad sense. Therefore, the method of assisting the diagnosis of the onset risk of broadly defined POAG of the present invention is also a method of providing information on the onset risk of broadly defined POAG, and also a method of determining, evaluating, or inspecting the onset risk of broadly defined POAG. be. In the present specification, the term "polymorphism" or "variant" means that there is diversity in the base sequence and structure (insertion / deletion, inversion, number of copies) at a specific position of the genome in a certain organism species. The site where a polymorphism is present (hereinafter, also referred to as a polymorphism site) is a site on the genome in which a variant such as a single nucleotide polymorphism (SNP) is recognized.

In the present invention, the "allele" refers to each type having different bases that can be taken at a certain polymorphism site, and the "risk allele" is a non-broad definition among alleles of SNP related to POAG in a broad sense. In a broader sense than the POAG patient group, it refers to an allele that is more frequent in the POAG patient group, and "non-risk allele" refers to an allele that is not a risk allele.

In the present invention, the "risk of developing POAG in a broad sense" is a risk related to POAG in a broad sense, and refers to the possibility of developing POAG in a broad sense in the future, which is determined by disease susceptibility. In the present invention, risk prediction means determining the presence or absence of future risk at the present time, or determining the magnitude of future risk at the present time.

The method of assisting the diagnosis of the onset risk of POAG in the broad sense of the present invention is
Allele measurement process, which measures alleles for a specific SNP,
It includes an information acquisition step of acquiring information on a subject based on the measurement result of the allele, and an information providing step of providing information for determining the onset risk of the subject based on the information. Each step will be described below in order, but first, a method for identifying an SNP group related to the onset of POAG in a broad sense, which is a measurement target in the present invention, will be described. The SNP group associated with the onset of POAG in a broad sense may be referred to as the SNP group of the present invention.

(Identification of SNP group associated with the onset of POAG in a broad sense)
In the present invention, the SNP group associated with the onset of broad-sense POAG is specifically, first, a glaucoma patient diagnosed with broad-sense POAG (sometimes simply referred to as a patient), and a diagnosis that is not broad-sense POAG. Genomic DNA is extracted from healthy non-glaucoma patients (sometimes simply referred to as non-patients, controls, or non-broadly-defined POAG patients) who have been judged by interview to have no family history of glaucoma. Then, by comparing and variously analyzing the allele frequency in each SNP between the patient group and the non-patient group using about 200,000 to 5 million known SNPs on the human genome as an index, the difference in frequency is statistically high. Find a group of SNPs that are significant marker candidates. Then, by further analyzing from this candidate group, the SNP group (marker SNP group) used in the present invention is determined, and from among them, a specific SNP group (core SNP group) that gives a more accurate determination result. By setting the remaining SNP group (pool SNP group) and using these in combination, it is possible to determine the presence or absence of the risk of developing POAG in the broad sense and predict the magnitude of the risk of developing POAG in the broad sense. Although the details will be described in the section of Examples, the SNP group related to the broadly defined POAG disclosed in the present invention can be identified by the following method.

(1) Method for finding SNP groups of marker candidates Extract genomic DNA from the blood of each patient group and non-patient group. Genomic DNA in blood can be extracted by any known method. For example, DNA obtained by dissolving and eluting cells is bound to the surface of magnetic beads coated with silica and separated by magnetism. DNA can be extracted by recovery.

The means for identifying the allele in the SNP in the extracted DNA sample is not particularly limited, and an appropriate selection may be made from the SNP detection method and the SNP typing method known in the art.

Here, a method using genome-wide association study (GWAS) will be described. Specifically, for example, a DNA microarray containing SNPs distributed in the entire genome region (Affymetrix, Genome-Wide Human SNP Array 6.0) can be used. At that time, the SNP to be extracted may be selected by performing Quality control.

As the call rate as a criterion for acceptance or rejection of SNP in quality control, for example, it is desirable to adopt an SNP having a call rate of preferably 85% or more, more preferably 90% or more, still more preferably 95% or more. In addition, SNPs with a minor allele frequency (MAF) of less than 0.01 and SNPs whose genotype distribution deviates significantly from Hardy-Weinberg equilibrium (HWE) (false discovery rate is less than 0.001) are among the candidates. It is desirable to exclude it.

The SNPs selected in Quality control will be further narrowed down. Specifically, for example, by performing a chi-square test using statistical software, the P value is preferably 1 × 10 ^-3 or less, more preferably 3 × 10 ^-4 or less, still more preferably 1 × 10 ^-4 . Select an SNP that meets the following:

Next, for the extracted SNPs, a two-dimensional cluster plot analysis for excluding genotyping defective SNPs, for example, a cluster plot image obtained from genotyping software (Genotyping Console) is visually observed. Excludes SNPs with poor genotyping and determines the SNP group of marker candidates.

The SNPs selected in this way can be found in known sequences such as GenBank and dbSNP, or by referring to a database of known SNPs, to the position on the genome where the SNP is present, sequence information, the gene in which the SNP is present, or the vicinity thereof. Information such as existing genes, distinction between introns or exones when they are present on genes, their functions, and homologous genes in other species can be obtained.

(2) Method for finding marker SNP group Next, for the marker candidate SNP group obtained above, genotype data is quantified and extracted.

In quantification, refer to the genotype data and risk allele database, for example, in a predetermined allele contained in the genotype data, if the risk allele is homozygous, the numerical value is 2, and if the risk allele is heterozygous, the numerical value is 2. 1 is given, and when the non-risk allele is homozygous, a numerical value of 0 is given. Then, the obtained numerical values are normalized by the following formulas using the average value of the appearance frequency of each allele and the observed frequency to create a quantified genotype data matrix in the selected SNP group. ..

The risk allele is an allele that frequently appears in a patient group. In the present invention, risk alleles are defined based on odds ratios. The odds ratio is generally the ratio of the proportion of people with and without risk factors in the patient group, that is, the odds divided by the similarly determined odds in the non-patient group, as in the present invention. Often used in case control studies. In the present invention, the odds ratio is determined based on the allele frequency, and can be calculated by dividing the ratio of one allele to another in the patient group by the ratio of the frequencies similarly obtained in the non-patient group. can. The frequency of appearance of each allele can be recalculated at any time by additionally updating the data as the data of the sample provider is acquired, for example.

Next, a cluster analysis is performed using the digitized genotype data matrix. Specifically, for example, considering the linkage disequilibrium (LD) between SNPs, the SNPs that are considered to have high independence are determined by principal component analysis (PCA). As a method for principal component analysis, a known method can be used. For example, for the SNP selected above, information reduction is performed in each of the entire genome or chromosome to perform factor loading (principal component and original). The marker SNP group of the present invention is selected by calculating (corresponding to the correlation coefficient between variables), determining a candidate region based on the calculation, and selecting the SNP having the lowest P value in the region as a candidate SNP. decide. However, duplicate SNPs are excluded from the calculation from the whole genome and the calculation from each of the chromosomes.

Next, a method of further selecting a specific SNP group that contributes to highly accurate determination from the marker SNP group of the present invention will be described. Hereinafter, such an SNP group will be referred to as a core SNP group.

(3) Method for finding core SNP group and pool SNP group For the core SNP group, about 50 SNP groups are selected from the marker SNP group obtained above in ascending order of P value, and new SNP groups are selected for the selected SNP group. It can be determined by performing a reproduction experiment by a group. The pool SNP group is a group obtained by excluding the core SNP group from the marker SNP group.

More specifically, for the SNP group selected as described above, identification of alleles using DNA collected from a group different from the patient group and the non-patient group used for identifying the marker candidate SNP group. I do. Here, the different group is a group in which completely non-identical is preferable, although some overlap may occur. As the analysis method, for example, a mass spectrometry method is preferable, and a known Mass ARRAY can be used. Quality control may be performed, and as the call rate at that time, for example, an SNP having a call rate of preferably 85% or more, more preferably 90% or more, still more preferably 95% or more may be adopted. desirable. In addition, SNPs with a minor allele frequency (MAF) of less than 0.01 and SNPs whose genotype distribution deviates significantly from Hardy-Weinberg equilibrium (HWE) (false discovery rate is less than 0.001) are among the candidates. It is desirable to exclude it.

Then, for the identified SNPs, a two-dimensional cluster plot analysis is performed in the same manner as described above to exclude genotyping defective SNPs.

For the SNPs selected in this way, for example, by performing a chi-square test using genotyping software, the P value is preferably 1 × 10 ^-3 or less, more preferably 3 × 10 ^-4 or less, still more preferable. Selects SNPs that satisfy 1 × 10 ^-4 or less.

Since it is preferable to integrate and judge the results of two analyzes for the selected SNP group, the analysis results should be integrated and evaluated by a known meta-analysis method, for example, the Cochran-Mantel-Henzel method. Can be done. Then, the SNPs having a P value of 3 × 10 ^-3 or less according to the analysis method are defined as the core SNP group (12 in total) in the present invention, and the remaining SNP groups (471 in total) that do not correspond to the core SNP group are pooled SNPs. Make a group. Table 1 shows the probe sequences containing SNPs in the core SNP group, and Table 2 (Tables 2-1 to 2-24) shows the probe sequences containing SNPs in the pooled SNP group. In the table, alleles of each SNP are described in parentheses in the probe sequence, and the sequence containing the risk allele is designated as SEQ ID NO: A and the sequence containing the non-risk allele is designated as SEQ ID NO: B.

Using the SNP group of the present invention thus selected, the following steps are performed.

[Allele measurement process]
In the allele measurement step, the core SNP group is always measured, and the SNP selected from the pool SNP group (pool selection SNP group) is also measured. The number of SNPs to be measured is not particularly limited as long as the total number including the core SNP group is at least 30, and for example, when the measurement target is 30, it is composed of 12 core SNPs and 18 pool selection SNPs. To. If the measurement target is 40, it is composed of 12 core SNPs and 28 pool selection SNPs. If the measurement target is 50, it is composed of 12 core SNPs and 38 pool selection SNPs. If the measurement target is 90, it is composed of 12 core SNPs and 78 pool selection SNPs.

Further, in the present invention, one measurement result may be used to proceed to the next information acquisition step, but from the viewpoint of improving the determination accuracy, the present invention has a plurality of results obtained by performing a plurality of measurement steps. You can proceed to the information acquisition process. In this case, for the core SNP group consisting of 12 SNPs , all 12 can be used as measurement targets without duplication in any of the measurement steps. The number of core SNP groups per step may be the same or different. Further, the number of SNPs to be measured for each step may be the same or different, and is not particularly limited as long as the total number including the core SNP group is at least 30 in each step.

Specifically, for example, when the allele measurement step includes two measurement steps,
A first SNP containing at least 30 SNPs including one or more SNPs selected from the core SNP group (first core SNP group) and a plurality of SNPs selected from the pool SNP group (first pool selection SNP group). The first measurement step to measure the group and
A total of at least 30 SNPs including one or more SNPs different from the first core SNP group (second core SNP group) and a plurality of SNPs selected from the pool SNP group (second pool selection SNP group) are included. A step including a second measurement step for measuring the second SNP group is exemplified, and here, the first pool selection SNP group and the second pool selection SNP group are not the same. For example, the number of SNPs to be measured in each measurement step is 6 in the 1st core SNP group, 24 or more in the 1st pool selection SNP group, and the 2nd core SNP group in the 2nd SNP group. There are 6 cases, and there are 24 or more examples of the second pool selection SNP group. In addition, in this specification, "non-identical" means that they are not completely the same. For example, only one may be different, or the constituent SNPs of the pool selection SNP group may be all different. ..

Also, if the allele measurement process involves three measurement steps,
A first SNP containing at least 30 SNPs including one or more SNPs selected from the core SNP group (first core SNP group) and a plurality of SNPs selected from the pool SNP group (first pool selection SNP group). The first measurement step to measure the group and
A total of at least 30 SNPs including one or more SNPs different from the first core SNP group (second core SNP group) and a plurality of SNPs selected from the pool SNP group (second pool selection SNP group) are included. The second measurement step for measuring the second SNP group, and
At least one or more SNPs (third core SNP group) different from the first core SNP group and the second core SNP group and a plurality of SNPs selected from the pool SNP group (third pool selection SNP group) are combined. An example is a step including a third measurement step of measuring a third SNP group containing 30 SNPs. Here, the first pool selection SNP group, the second pool selection SNP group, and the third pool selection SNP group are defined. Both are non-identical. To exemplify the number of SNPs to be measured in each measurement step, for example, the first core SNP group in the first SNP group has four, the first pool selection SNP group has 26 or more, and the second core in the second SNP group. There are 4 SNP groups, 26 or more selected SNP groups in the second pool, 4 in the 3rd core SNP group in the 3rd SNP group, and 26 or more in the selected SNP group in the 3rd pool.

In the present invention, if necessary, the fourth and subsequent measurements can be performed.

The biological sample used in the present invention may be any sample as long as it can extract DNA derived from the genome. For example, whole blood, whole blood cells, leukocytes, lymphocytes, plasma, serum, lymph, tears, saliva, nasal juice, cerebrospinal fluid, bone marrow, semen, sweat, mucosal tissue, skin tissue, hair roots and the like are used. As the method for extracting DNA from such a biological sample, any known method can be adopted.

For the SNP group to be measured, the allele is determined according to a known method and the measurement result is obtained. Specifically, for example, each allele-specific probe (Tables 1 to 2) designed based on the sequence information of the SNP group of the present invention is hybridized and the signal is detected. Alleles can be detected. Examples of methods for hybridizing using a probe include a Tuckman method, an Invader (registered trademark) method, a light cycler method, a cyclone probe method, an MPSS method, a bead array method, a DNA chip method, and a microarray method. It is also possible to detect alleles without hybridization with a probe, and for example, PCR-RFLP method, SSCP method, mass spectrometry method, next-generation sequencing method, direct sequencing method and the like can be used. These methods can be performed according to known conditions.

The measurement result thus obtained is used for the next information acquisition step.

[Information acquisition process]
In the information acquisition process, information on the risk of developing POAG in a broad sense is acquired based on the allele to be measured. The allele information may be information on whether or not a variant exists, information on the measured allele type, or information obtained from the number of alleles. Further, it may be calculated as a value (statistical value or the like) that correlates with SNP. Above all, in the present invention, from the viewpoint of improving the determination accuracy, it is preferable to determine whether or not the measured allele is a risk allele and count the total number of risk alleles. That is, based on the risk allele data acquired in advance, it is determined whether or not the measured allele is a risk allele, and the total number of alleles determined to be risk alleles in the entire SNP group to be measured (risk allele possession). Count). When a plurality of measurement steps are performed, positive or negative is determined based on whether the total number of risk alleles exceeds a predetermined threshold value set in advance in each measurement, and the number thereof is counted. When the total number of risk alleles thus obtained and the number of positives and negatives of each measurement result are performed in multiple measurement steps, the number of positives and negatives of each measurement result is further certified as a quantitative value of the sample provider, and the following information is obtained. It is preferable to proceed to the providing process.

[Information provision process]
In the information providing process, information for determining the onset risk of the subject based on the obtained information is provided to the subject.

As a method for determining the onset risk, for example, when the information obtained by the above step is the number of risk alleles possessed, four specific embodiments can be mentioned.
Aspect 1: The presence or absence of the risk of onset is determined by the numerical value of the number of possessed risk alleles Aspect 2: The presence or absence of the onset risk is determined by integrating the presence or absence of the onset risk when a plurality of possessed risk alleles are obtained Aspect 3: The number of possessed risk alleles Aspect for calculating the probability of having an onset risk based on

As the method of the first aspect, for example, the result (number of risk alleles possessed) related to the subject obtained by the information acquisition step is determined in advance by ROC (Receiver Operating Characteristic) analysis based on the SNP used in the allele measurement step. If it exceeds the cutoff value, the subject has a high risk of developing POAG in a broad sense, and if it falls below the cutoff value, a method of providing information that the risk is low can be mentioned.

More specifically, first, the number of risk alleles possessed by the sample provider is compared with a preset threshold value. The threshold value is an appropriate cutoff value that distinguishes between a patient and a non-patient, and by comparing the threshold value with a quantitative value, it can be determined whether or not there is a risk of developing POAG in a broad sense.

The threshold can be set as follows. For the same SNP group as the SNP group selected as the measurement target when acquiring the quantitative values of the sample provider, we used biological samples collected from subjects who were previously diagnosed as having a risk of developing POAG in a broad sense. Then, by measuring the number of risk alleles as described above and statistically processing "presence or absence of risk of developing POAG in a broad sense" and "number of risk alleles", the correlation between the two data is analyzed. From the analyzed results, for example, whether to emphasize the high true positive rate (high sensitivity), the high true negative rate (high specificity), or the true positive rate and the true negative. The threshold value can be set according to the purpose such as how much the rate is balanced. That is, if the SNP group to be measured is different, the risk allele existing there is naturally different, so that the threshold value varies depending on the SNP group to be measured. Here, the true positive rate is the probability of correctly determining a person who has a risk of developing POAG in a broad sense as a person who has a risk of developing POAG in a broad sense, and the true negative rate is a person who does not have a risk of developing POAG in a broad sense. It is the probability of correctly determining a person as a person who does not have the risk of developing POAG in a broad sense.

As a specific threshold setting method, first, for the same SNP group as the SNP group selected as the measurement target when acquiring the quantitative value of the sample provider, the vertical axis is the true positive rate (sensitivity) and the horizontal axis is the horizontal axis. Create an ROC curve created by taking the true negative rate (1-specificity) (perform ROC analysis). Next, the point where the distance from the upper left corner of the graph is the minimum may be the threshold value, and the point farthest from the diagonal line where the area under the curve (AUC) is 0.5 may be the threshold value, which is arbitrary. A point that has the specificity and sensitivity of may be set as a threshold value. In the present invention, it is preferable to set a threshold value at which the sensitivity is 1 and (1-specificity) is the closest to 0. For example, [(1-sensitivity) ² + (1-specificity) ² ] is the minimum. Can be set as a threshold value.

The threshold value may be acquired separately at the same time when the quantitative value of the sample provider is acquired, or may be acquired in advance. Further, when comparing with the quantitative value of the sample provider, the analysis result obtained so far may be additionally updated at any time and obtained.

Further, in setting the threshold value, normalization or weighting may be performed from the viewpoint of improving the determination accuracy. Specifically, for example, as a normalization method, a method of comparing with a normal distribution curve can be used. Further, as a weighting method, weighting can be performed in consideration of the odds ratio of each SNP.

By comparing the preset threshold value with the quantitative value of the sample provider in this way, it is possible to determine whether or not the sample provider has a risk of developing POAG in a broad sense.

As a method of the second aspect, for example, when the allele measurement step includes a plurality of measurement steps, the result (number of each risk allele possessed) in each measurement step regarding the subject obtained by the information acquisition step is obtained in each measurement step. As an index of whether or not the cutoff value previously determined by ROC analysis is exceeded based on the SNP group used in the above, there is a method of providing information on the high or low risk of the subject developing POAG in a broad sense. Be done.

More specifically, first, the presence or absence of risk of onset is determined by comparing the number of risk alleles possessed by the sample provider with a preset threshold value for each measurement step. The threshold value can be set in the same manner as in the first aspect. Next, the results of the presence or absence of onset risk for each measurement step are integrated. Specifically, for example, when the measurement step is performed three times and the case where there is a risk of onset is displayed as "+" and the case where there is no risk of onset is displayed as "-", the first judgment result is "+" and the second judgment. The result is "+", the third judgment result is "+", the integrated result is "+++", the first judgment result is "+", the second judgment result is "+", and the third judgment result. However, the integration result of "-" is "++-", the first judgment result is "+", the second judgment result is "-", and the third judgment result is "+". It is classified as "-+". Therefore, even if the probability of being negative once in three judgments is the same, "++-" and "++-" correspond to different classifications. Then, using the obtained integrated results, is the risk of developing POAG in a broad sense high based on the tendency to fall under the same classification on the graph of the group with the risk of onset and the group without the risk of onset that are known in advance? Determine if it is low.

The graph of the group with the onset risk and the group without the onset risk can be created as follows. For example, by obtaining integrated results for pre-diagnosed subjects, the number of people who have a risk of developing POAG in a broad sense is accumulated for each pattern such as "++++", "++-", and "++-". It can be created by doing.

In the graph of the group with the onset risk and the group without the onset risk prepared in advance in this way, the sample provider has the onset risk of POAG in a broad sense by obtaining the information of the corresponding category from the integration result of the sample providers. It can be determined whether or not.

In addition, in aspect 2, the probability of having a risk of developing POAG in a broad sense may be calculated by applying Bayes' theorem described later.

As the method of the third aspect, for example, a method of applying Bayes' theorem to the result obtained in the information acquisition step to calculate the probability of having a risk of developing POAG in a broad sense can be mentioned. Generally, in clinical practice, it is unclear whether or not the test subject has a risk of developing the disease, and the presence or absence of the risk of developing the disease is estimated from the test results. Therefore, the positive predictive value of the test method is used. High (PPV) and negative predictive value (NPV) are also desired. Here, the positive predictive value is the percentage of those who have a disease when the test result is positive, and the negative predictive value is the percentage of those who do not have the risk of developing the disease when the test result is negative. Is. By presenting it as the probability of having a broad-sense POAG onset, it is easier to understand that the higher the probability, the higher the risk of developing a broad-sense POAG.

As a specific method for calculating the probability of having a risk of developing POAG in a broad sense, the result obtained in the information acquisition process (number of risk alleles possessed) is compared with the threshold in the same manner as in Phase 1, so that the risk of developing POAG in a broad sense can be determined. The presence or absence is determined, and the method of Bayes' theorem is applied to the result to calculate the probability of having the risk of developing the disease. In the method of the Bayes theorem, the posterior probability (positive predictive value, negative predictive value) can be obtained by combining the prior probability (prevalence) with the sensitivity and specificity described above. The probability of developing POAG in a broad sense is expressed as a posterior probability. In other words, if the method of Bayes'theorem is not used, the probability of having the risk of developing the disease is the same as the prevalence, but if the test gives a positive result by using the method of Bayes' theorem, the test target. It means that the probability of a person's risk of developing the disease can be expressed by a positive predictive value. Specifically, positive predictive value = prevalence x sensitivity / [prevalence x sensitivity + (1-prevalence) x (1-specificity)], negative predictive value = specificity x (1-). Prevalence) / [Specificity x (1-prevalence) + Prevalence x (1-sensitivity)], for example, the prevalence is 10% and the test sensitivity is 70. %, With a specificity of 70%, the positive predictive value is calculated to be 21% and the negative predictive value is calculated to be 95%. Therefore, since the risk of developing a test subject who gives a positive result is 21%, it is higher than the prevalence rate, and it can be advised to receive further medical examination. In addition, when the above tests were performed in combination three times, the positive predictive value was calculated to be 62% and the negative predictive value was calculated to be 99%. Therefore, the number of risk alleles exceeded the threshold value in the third test. The test subject can be determined to have a higher risk of developing POAG in a broad sense.

In this way, it is possible to make a judgment including the influence of the prevalence rate as the overall background of the test subject, and it is possible to present it as the probability of the onset risk of POAG in the broad sense of the sample provider.

As the method of aspect 4, for example, the Bayes' theorem is applied to the result obtained in the information acquisition process to calculate the probability of having an onset risk based on the number of risk alleles possessed (average onset risk, 95% credible interval), and further. A method of calculating the probability of having a risk of developing POAG in a broad sense with a probability of a preset percentage (%) or more can be mentioned. Here, as the percentage to be set, for example, an arbitrary numerical value such as 70%, 80%, 90% or the like can be set, and the larger the value, the higher the accuracy of the determination.

More specifically, for example, the risk of developing POAG in a broad sense according to Bayes' theorem for each pattern such as "+++", "++-", "++-", etc., which is the integrated result obtained from the pre-diagnosed subject used in the second aspect. Calculate the probability density function with. For example, when calculating the probability of 70% or more, the area within the range of the density in which the probability of having the onset risk, which is a continuous random variable, is 70 or more from the probability density function is the probability of having the onset risk.

Thus, in the present invention, the risk of developing POAG in the broad sense of the sample provider is not only determined whether or not the risk of developing POAG in the broad sense is present, but also by comparing the total number of risk alleles with the threshold value. By calculating the probability, it is possible to provide more detailed information on the risk of developing POAG in the broad sense of the sample provider.

The present invention also provides an apparatus for acquiring information on the risk of developing POAG in a broad sense.

The apparatus of the present invention includes a processor and a computer having a memory under the control of the processor, and the memory includes the following steps:
Selected from the core SNP group consisting of 12 SNPs shown in Table 1 and the pooled SNP group shown in Table 2 based on the allelic information of single nucleotide polymorphisms (SNPs) in biological samples collected from subjects. Allele measurement process for measuring alleles for at least 30 SNPs including SNPs (pool selection SNP group),
Based on the measurement result of the allele, the information acquisition process for acquiring information on the risk of developing POAG in the broad sense of the subject, and based on the information obtained above, the risk of developing POAG in the broad sense of the subject is determined. A computer program for causing the computer to execute an information providing process for providing information for determination is recorded.

The present invention also includes a computer program for causing a computer to determine the risk of developing POAG in a broad sense in a subject. For example, such a computer program is as follows.

A computer program recorded on a computer-readable medium that involves the following steps:
Selected from the core SNP group consisting of 12 SNPs shown in Table 1 and the pooled SNP group shown in Table 2 based on the allelic information of single nucleotide polymorphisms (SNPs) in biological samples collected from subjects. Allele measurement process for measuring alleles for at least 30 SNPs including SNPs (pool selection SNP group),
Based on the measurement result of the allele, the information acquisition process for acquiring information on the risk of developing POAG in the broad sense of the subject, and based on the information obtained above, the risk of developing POAG in the broad sense of the subject is determined. The information providing process for providing information for determination is executed, and the risk of developing POAG in a broad sense is determined in the subject.

The medium may be a medium on which the computer program is non-temporarily recorded and readable by a computer.

Hereinafter, an example of an apparatus suitable for carrying out the method of the present invention will be described with reference to the drawings. However, this embodiment is not limited to this example. FIG. 1 is a schematic view showing an example of a device for determining the risk of developing POAG in a broad sense in a subject. The determination device 10 shown in FIG. 1 includes a measuring device 20 and a computer system 30 connected to the measuring device 20.

In this embodiment, the measuring device 20 is a scanner or a mass spectrometer that detects a signal based on DNA bound to a probe on a microarray. In this embodiment, the signal is optical information such as a fluorescent signal or the result of mass spectrometry. When the microarray in contact with the measurement sample is set in the measuring device 20, the measuring device 20 acquires optical information or mass spectrometric analysis results based on the nucleic acid derived from the biological sample of the subject bound to the probe on the microarray. , The obtained optical information or mass spectrometry result is transmitted to the computer system 30.

The scanner is not particularly limited as long as it can detect a signal based on the DNA bound to the probe on the microarray. Since the signal differs depending on the labeling substance used for labeling the DNA derived from the biological sample of the subject, the scanner can be appropriately selected according to the type of the labeling substance. For example, when the labeling substance is a fluorescent substance, a microarray scanner capable of detecting the fluorescence generated from the fluorescent substance is used as the measuring device 20.

When the SNP is detected by the next-generation sequence method or the direct sequence method, the measuring device 20 may be a device including a DNA amplification device and a sequence analysis device. In this case, a reaction solution containing a measurement sample, an enzyme for DNA amplification, a primer and the like is set in the measuring device 20, and the DNA in the reaction solution is amplified by the DNA amplification method. Then, the measuring device 20 analyzes the base sequence of the amplification product, acquires sequence information, and transmits the obtained sequence information to the computer system 30.

The computer system 30 includes a computer main body 300, an input unit 301, and a display unit 302 for displaying sample information, determination results, and the like. The computer system 30 receives optical information, mass spectrometry results, or sequence information from the measuring device 20. Then, the processor of the computer system 30 executes a program for determining the risk of developing POAG in a broad sense in the subject based on the optical information, the mass spectrometry result, or the sequence information. As shown in FIG. 1, the computer system 30 may be a device separate from the measuring device 20 or a device including the measuring device 20. In the latter case, the computer system 30 may itself be the determination device 10.

As shown in FIG. 2, the computer main body 300 includes a CPU (Central Processing Unit) 310, a ROM (Read Only Memory) 311, a RAM (Random Access Memory) 312, a hard disk 313, an input / output interface 314, and the like. It includes a reading device 315, a communication interface 316, and an image output interface 317. The CPU 310, ROM 311, RAM 312, hard disk 313, input / output interface 314, read device 315, communication interface 316, and image output interface 317 are connected by a bus 318 so as to be capable of data communication. Further, the measuring device 20 is communicably connected to the computer system 30 by the communication interface 316.

The CPU 310 can execute the program stored in the ROM 311 and the program loaded in the RAM 312. The CPU 310 calculates the effectiveness prediction value, reads the discriminant stored in the ROM 311 and determines the effectiveness. The CPU 310 outputs the determination result and displays it on the display unit 302.

The ROM 311 is composed of a mask ROM, a PROM, an EPROM, an EEPROM, and the like. As described above, the program executed by the CPU 310 and the data used for the program are recorded in the ROM 311. A predetermined threshold value or the like may be recorded in the ROM 311.

The RAM 312 is composed of SRAM, DRAM, and the like. The RAM 312 is used to read the program recorded in the ROM 311 and the hard disk 313. The RAM 312 is also used as a work area for the CPU 310 when executing these programs.

The hard disk 313 is installed with an operating system for the CPU 310 to execute, a computer program such as an application program (a computer program for determining the risk of developing POAG in a broad sense), and data used for executing the computer program. A predetermined threshold value or the like may be recorded on the hard disk 313.

The reading device 315 is composed of a flash memory, a flexible disc drive, a CD-ROM drive, a DVD ROM drive, and the like. The reading device 315 can read the program or data recorded on the portable recording medium 40. It may also be described as a reading device.

The input / output interface 314 is composed of, for example, a serial interface such as USB, IEEE1394, RS-232C, a parallel interface such as SCSI, IDE, IEEE1284, and an analog interface including a D / A converter and an A / D converter. It is configured. An input unit 301 such as a keyboard and a mouse is connected to the input / output interface 314. The operator can input various commands to the computer main body 300 by the input unit 301.

The communication interface 316 is, for example, an Ethernet (registered trademark) interface or the like. The computer body 300 can also transmit print data to a printer or the like by the communication interface 316.

The image output interface 317 is connected to a display unit 302 composed of an LCD, a CRT, or the like. As a result, the display unit 302 can output a video signal corresponding to the image data given by the CPU 310. The display unit 302 displays an image (screen) according to the input video signal.

Next, a processing procedure for determining the high or low risk of developing POAG in a broad sense by the determination device 10 will be described. Here, a case where risk allergen information is acquired from fluorescence information based on DNA derived from a biological sample of a subject bound to a probe on a microarray and a judgment is performed using the obtained measured values will be described as an example. However, this embodiment is not limited to this example.

With reference to FIG. 3, in step S101, the CPU 310 of the determination device 10 acquires fluorescence information from the measuring device 20. Next, in step S102, the CPU 310 calculates the fluorescence intensity from the acquired fluorescence information and stores it in the RAM 312. Then, in step S103, the CPU 310 determines the presence or absence of each variant and its type from the fluorescence intensity stored in the RAM 312, and calculates the total number of risk alleles according to the allele data stored in the ROM 311 or the hard disk 313.

Then, in step S104, the CPU 310 uses the calculated effectiveness prediction value and a predetermined threshold value stored in the ROM 311 or the hard disk 313 to determine the high or low risk of developing POAG in a broad sense in the subject. Here, when the total number of risk alleles is smaller than a predetermined threshold value, the process proceeds to step S105, and the CPU 310 stores in the RAM 312 a determination result indicating that the risk of developing POAG in a broad sense in the subject is low. On the other hand, when the total number of risk alleles is not lower than a predetermined threshold value (that is, when the total number of risk alleles is equal to or higher than the threshold value), the process proceeds to step S106, and the CPU 310 has a risk of developing POAG in a broad sense in the subject. The determination result indicating that the value is high is stored in the RAM 312.

Then, in step S107, the CPU 310 outputs the determination result and displays it on the display unit 302 or prints it on the printer. This makes it possible to provide doctors and the like with information that assists in determining whether or not the subject has a high risk of developing POAG in a broad sense.

Hereinafter, the present invention will be specifically described with reference to examples. This example is merely an example of the present invention and does not mean any limitation. In the following examples, molecular cloning (Joseph Sambrook et al., Molecular Cloning --A Laboratory Manual, 3rd Edition, Cold Spring Harbor Laboratory) will be described for commonly used molecular biology methods that are not described in detail. The methods and conditions described in the textbooks such as Press, 2001) are used.

Test Example 1 Selection of marker SNP (core SNP + pool SNP) 824 patients diagnosed with primary open-angle glaucoma in a broad sense (POAG patient group in a broad sense) and 824 patients diagnosed as not having glaucoma and family history of glaucoma by interview Total DNA was extracted from the blood of 686 non-patients who were determined not to have glaucoma using a commercially available automated nucleic acid extractor. Extraction of total DNA was performed according to the instruction manual of the device and kit. By this method, about 5 μg of total DNA was obtained from 350 μL of blood sample.

For SNP analysis, 906,600 SNP genotype data using a commercially available microarray type SNP analysis kit DNA microarray (Genome-Wide Human SNP Array 6.0) capable of analyzing about 900,000 known SNPs on the human genome. Was obtained, and 653,519 high-precision SNP data were selected using a QC filter (Call Rate, ≧ 0.95; MAF, ≧ 0.01; HWE, ≧ 0.001). Furthermore, 787 SNP marker candidate groups were extracted by the following process.
(1) P <0.001 was used as the extraction condition in genome-wide association analysis (χ ² test using allelic data).
(2) For all the extracted SNPs, cluster defective SNPs were excluded by visual judgment by three examiners based on 2D cluster plot images obtained from Affymetrix's Genotyping Console.

For the marker SNP, 483 markers were selected from the SNP marker candidate group by the following procedure. The marker SNP consists of 12 core SNPs and 471 pool SNPs.
(1) Genotyping data was coded (numerical conversion) and normalized according to the following procedure.
(a) Risk Allele Homo: 2, Risk Allele Hetero: 1, Other Allele Homo: 0.
(b) Numerical conversion was performed by normalizing the numerical values according to the above formula using the mean value and the observed allergen frequency in each group of the POAG patient group in the broad sense and the healthy non-glaucoma group.
(2) The combination of SNP marker candidate groups considering linkage disequilibrium (LD) was calculated by cluster analysis using principal component analysis (PCA).
(a) Using the SNP marker candidate group, all the samples from the broadly defined POAG group and the healthy non-glaucoma group were submitted to PCA, and the factor loadings (main component and original) obtained by information reduction (Cluster SNP) with the samples. Corresponds to the correlation coefficient with variables) was calculated. Next, candidate regions were determined by clusters based on the principal component showing the absolute value of the highest factor loading of each SNP, and the SNP that obtained the smallest P value in each candidate region was used as a marker SNP candidate. ..
(b) Using the SNP marker candidate group in "each chromosome", the candidate region was determined in each chromosome in the same manner as in (a), and the candidate region was used as a marker SNP candidate.
(c) The marker SNP candidates of (a) and (b) were combined to eliminate duplication.

Acquisition of core SNPs Using the top 51 marker SNPs, a mass array reproduction experiment was performed using 1,492 patients with POAG in a broad sense and 1,052 healthy non-glaucoma patients from another group. A related analysis (χ ² test with allelic data) was performed using the SNP group that passed through the QC filter (Call Rate, ≧ 0.9; MAF, ≧ 0.01; HWE, ≧ 0.001). Regarding the reproducibility of genome-wide association studies and reproduction experiments using mass arrays, 12 SNPs based on the Cochran-Mantel-Henzel test results (P <0.003) were used as core SNPs.

Acquisition of pooled SNP The group obtained by removing the core SNP from the marker SNP was defined as the pooled SNP.

Example 1 and Comparative Example 1
For 680 patients in the broadly defined POAG group and 680 patients in the non-glaucoma healthy subject group, blood was collected and genomic DNA was extracted in the same manner as in Test Example 1, and Example 1 (Table 3) and Comparative Example 1 (Table 4-1 to 1). Allele data were obtained by hybridization using the probes shown in Table 4-2), and the total number of risk alleles was counted. The probes used in Example 1 are the 12 probes shown in Table 1 and the 18 probes selected from Table 2, and the probes used in Comparative Example 1 are the top 30 probes in the assay. ..

A frequency distribution map with the total number of risk alleles on the horizontal axis is created, and the results of ROC analysis are shown in FIG. From FIG. 4, in Comparative Example 1, the sensitivity is 54.3% and the specificity is 65.4%, and the AUC is about 0.641 when the cutoff value is 42, whereas in Example 1, the sensitivity is 70.1% and the specificity is 71.2%. When a certain cutoff value is 37, the AUC is as high as 0.784, and it can be seen from the frequency distribution chart that the group with the onset risk and the group without the onset risk can be distinguished.

Example 2
Data acquisition was performed in the same manner as in Example 1 except that the probe used was different from that in Example 1. Specifically, the probes shown in Table 5 below were used.

(Aspect 1)
Measurements were performed using all 90 probes (12 in the core SNP group and 78 in the pool SNP group) shown in Table 5 above, and the total number of risk alleles was counted. A frequency distribution map with the total number of risk alleles on the horizontal axis is created, and the results of ROC analysis are shown in FIG. From Fig. 5, the AUC is 0.908 when the cutoff value is 108 with a sensitivity of 83.1% and a specificity of 82.1%, and the frequency distribution chart also distinguishes between the group with the risk of onset and the group without the risk of onset. You can see that it will be attached.

(Aspect 2)
Next, the probes used in the first aspect were divided into 30 probes, and the measurement steps were divided into three groups of measurement step 1, measurement step 2, and measurement step 3, and the total number of risk alleles was counted.

For the obtained results, the frequency distribution map of the risk allele and the ROC curve were obtained in the same manner as described above. The results are shown in FIG. From FIG. 6, the result of the first measurement is the sensitivity of 69.1% and the cutoff value at the specificity of 71.2% is 36, and the result of the second measurement is the cutoff value at the sensitivity of 74.1% and the specificity of 66.5%. The number was 37, and the result in the third measurement was 68.8% for the sensitivity and 36 for the cutoff value at the specificity of 75.4%, and it was found that the judgment results in each measurement could be obtained.

Further, FIG. 7 shows the result of integrating the determination results of the above three measurements. That is, the results of classifying the determination results of the above three measurements into a group having an onset risk and a group having no onset risk are shown. From this, it can be seen that there is a distinction between the group having the risk of developing the disease and the group having no risk of developing the disease.

(Aspects 3 and 4)
In the same manner as in aspect 2, after obtaining the judgment results for each group, the number probability (mean risk of onset, 95% credible interval) having the onset risk based on the number of risk alleles possessed was calculated using Bayes' theorem. .. In addition, using the Bayesian theorem, we calculated the area within the range of the density at which the probability of having the probability of developing the probability density function is 70 or more, and showed the probability of the risk of developing it with a probability of 70% or more. The results are shown in FIG.

The analysis using Bayes' theorem was performed according to the following procedure.
(a) Prior distribution π (θ) is a uniform distribution (non-information prior distribution). The beta distribution was adopted as the prior distribution.
(b) For the likelihood, 680 patients in the patient group and 680 patients in the non-patient group were randomly selected from all the samples in the patient group and the non-patient group, and the number of data (observations) and the number of patients (positive) for each risk allele possession number. Calculated from the number). The distribution adopted the binomial distribution.
(c) The posterior distribution was calculated from Bayes' theorem (posterior distribution π (θ | D) ∝ prior distribution × likelihood).
(d) From the posterior distribution, the mean onset risk (%), 95% credible interval (%), and probability of having onset risk (%) were calculated.

Example 3
Data acquisition was performed in the same manner as in Example 1 except that the probes used in Examples 1 and 2 were different. Specifically, the probes shown in Tables 6-1 to 6-2 below were used.

(Aspect 1)
Measurements were performed using all 120 probes (12 in the core SNP group and 108 in the pool SNP group) shown in Tables 6-1 to 6-2 above, and the total number of risk alleles was counted. FIG. 9 shows the frequency distribution map of the risk allele and the result of ROC analysis in the same manner as in Example 1. From Fig. 9, the AUC is 0.929 when the cutoff value is 141 with a sensitivity of 84.4% and a specificity of 85.3%, and the frequency distribution chart also distinguishes between the group with the risk of onset and the group without the risk of onset. You can see that it will be attached.

(Aspect 2)
Next, the probes used in the above aspect 1 were divided into 40 pieces each, and the measurement steps were divided into three groups of measurement step 1, measurement step 2, and measurement step 3, and the total number of risk alleles was counted.

For the obtained results, the frequency distribution map of the risk allele and the ROC curve were obtained in the same manner as described above. The results are shown in FIG. From FIG. 10, the result of the first measurement is the sensitivity of 74.4% and the cutoff value at the specificity of 72.6% is 44, and the result of the second measurement is the cutoff value at the sensitivity of 76.8% and the specificity of 69.7%. The number was 48, and the result in the third measurement was a sensitivity of 69.6% and a cutoff value of 50 at a specificity of 76.3%, and it was found that the judgment results in each measurement could be obtained.

Further, FIG. 11 shows the result of integrating the determination results of the above three measurements. That is, the results of classifying the determination results of the above three measurements into a group having an onset risk and a group having no onset risk are shown. From this, it can be seen that there is a distinction between the group having the risk of developing the disease and the group having no risk of developing the disease.

(Aspects 3 and 4)
In the same manner as in the second aspect, after obtaining the judgment result in each group, the average onset risk (%), the 95% credible interval (%), and the probability of having the onset risk (%) are the same as in the second embodiment. ) Was calculated. The results are shown in FIG. From this, it can be seen that there is a distinction between the group having the risk of developing the disease and the group having no risk of developing the disease.

Example 4
Data acquisition was performed in the same manner as in Example 1 except that the probes used in Examples 1 to 3 were different. Specifically, the probes shown in Tables 7-1 to 7-2 below were used.

(Aspect 1)
Measurements were performed using all 150 probes (12 in the core SNP group and 138 in the pool SNP group) shown in Tables 7-1 to 7-2 above, and the total number of risk alleles was counted. FIG. 13 shows the frequency distribution map of the risk allele and the result of ROC analysis in the same manner as in Example 1. From FIG. 13, the AUC is 0.940 when the cutoff value is 170 with a sensitivity of 89.0% and a specificity of 84.9%, and the frequency distribution chart also distinguishes between a group having an onset risk and a group having no onset risk. You can see that it will be attached.

(Aspect 2)
Next, the probes used in the first aspect were divided into 50 probes, and the measurement steps were divided into three groups of measurement step 1, measurement step 2, and measurement step 3, and the total number of risk alleles was counted.

For the obtained results, the frequency distribution map of the risk allele and the ROC curve were obtained in the same manner as described above. The results are shown in FIG. From FIG. 14, the result of the first measurement is the sensitivity 75.3% and the cutoff value at the specificity 74.3% is 53, and the result of the second measurement is the cutoff value at the sensitivity 69.6% and the specificity 78.4%. The number was 62, the result in the third measurement was sensitivity 75.6%, and the cutoff value at specificity 70.4% was 57, and it was found that the judgment results in each measurement could be obtained.

Further, FIG. 15 shows the result of integrating the determination results of the above three measurements. That is, the results of classifying the determination results of the above three measurements into a group having an onset risk and a group having no onset risk are shown. From this, it can be seen that there is a distinction between the group having the risk of developing the disease and the group having no risk of developing the disease.

(Aspects 3 and 4)
In the same manner as in the second aspect, after obtaining the judgment result in each group, the average onset risk (%), the 95% credible interval (%), and the probability of having the onset risk (%) are the same as in the second embodiment. ) Was calculated. The results are shown in FIG. From this, it can be seen that there is a distinction between the group having the risk of developing the disease and the group having no risk of developing the disease.

Example 5 Marker SNP Feedback Improvement Loop by Bayesian Approach Data Accumulation and Update by obtaining broadly defined POAG onset and negative results by post-judgment follow-up study of Example 2 or Example 3 or Example 4 ( Perform additional learning). Additional learning updates the Bayesian prior distribution π (θ) with the classification results newly obtained by the follow-up study.
-Perform a related analysis (χ ² test using allelic data) by adding the genotype data newly obtained by the follow-up study to the conventional results, and replace the pool SNP. In addition, when the replacement is carried out, the threshold value is reset by recalculating the ROC analysis of each measurement in the "information acquisition process", the prior distribution of Bayes in the "information providing process" is forgotten, and the uniform distribution (none). Recalculate as information prior distribution).
-If the result of the χ ² test using allelic data changes due to the accumulation and update of data, the marker candidate SNPs are replaced.

By the method of the present invention, by analyzing the allele of the SNP of the present invention on the DNA derived from the subject, it is possible to determine the high or low risk of developing open-angle glaucoma in the broad sense of the subject. Based on this risk, subjects can take preventive measures for broad-sense primary open-angle glaucoma or receive appropriate treatment, including preemptive treatment. Further, by using the SNP of the present invention to select a person having a high risk of developing open-angle glaucoma in a broad sense and conducting a clinical trial of a glaucoma therapeutic agent, the period of the clinical trial of the glaucoma therapeutic agent can be shortened. It is useful.

10 Judgment device 20 Measuring device 30 Computer system 40 Recording medium 300 Computer body 301 Input unit 302 Display unit 310 CPU
311 ROM
312 RAM
313 Hard disk 314 Input / output interface 315 Read device (reader)
316 communication interface 317 image output interface 318 bus

Claims (11)

SNPs (pools) selected from a core SNP group consisting of 12 SNPs and a pool SNP group consisting of 471 SNPs based on single nucleotide polymorphism (SNP) allele information in biological samples collected from subjects. Allele measurement process, which measures alleles for at least 30 SNPs including the selected SNP group).
Based on the measurement results of the allele, the information acquisition process for acquiring information on the risk of developing open-angle glaucoma in the broad-sense subject, and based on the information obtained above, the broad-sense nuclear power plant of the subject. A method of assisting a subject in diagnosing the risk of developing open-angle glaucoma in a broad sense, including an information providing step that provides information for determining the risk of developing open-angle glaucoma.
In the broad sense, if the information providing step exceeds the predetermined cutoff value based on the SNP used in the allergen measurement step for the result regarding the subject obtained by the information acquisition step, the subject is broadly defined. It is a step that includes a step of providing information that the risk of developing primary open-angle glaucoma is high, and if it is lower, the subject has a low risk of developing broad-sense primary open-angle glaucoma.
The 12 SNPs in the core SNP group are rs7623847, rs11159830, rs4852079, rs10853035, rs7531982, rs4430527, rs1399216, rs12437660, rs9442, rs1149332, rs11136906 and rs11956913.
471 SNPs in the pool SNP group are rs6429703, rs659046, rs11583644, rs835337, rs960501, rs3768184, rs6669702, rs2786755, rs1469876, rs2039153, rs12048011, rs9661521, rs7516960, rs515194, rs1127313, rs2661275, rs7524938 , Rs4233520, rs16850250, rs2841385, rs16855905, rs17313689, rs11680265, rs12615616, rs9332420, rs4344916, rs10490195, rs12713615, rs17030916, rs2587702, rs1529292, rs6747239, rs12477346, rs13408246, rs1627497, rs10804383 , Rs775779, rs775722, rs162871, rs1993802, rs1462793, rs4450855, rs9866028, rs7644682, rs1499787, rs9874964, rs7378503, rs7656362, rs956469, rs11945500, rs4295245, rs6814828, rs6837917, rs6847630, rs7664412, rs13 , Rs2036455, rs10011515, rs6835198, rs1392874, rs2567388, rs12641140, rs10049967, rs13118455, rs2675531, rs261133, rs261162, rs1501958, rs4701849, rs835136, rs865036, rs9688120, rs4354048, rs12659589, rs6883614 , Rs17097145, rs7744 710, rs197962, rs197963, rs2815019, rs4311505, rs11968166, rs1518516, rs4895745, rs7753862, rs1873329, rs9493858, rs9376182, rs646695, rs4394230, rs4535568, rs311330, rs311329, rs4870148, rs9480313, rs6908330 rs7788738, rs10282694, rs1123227, rs879965, rs17152102, rs6968827, rs6943901, rs1015573, rs12698976, rs10271370, rs2079162, rs10215082, rs1476446, rs6466117, rs2267889, rs6959243, rs4725651, rs3779853, rs6959243, rs4725651, rs3779853, rs4876223 rs11786580, rs17250119, rs2853236, rs13248227, rs729005, rs16929, rs273393, rs2221770, rs7009780, rs10815523, rs1331260, rs10815020, rs10974623, rs10974624, rs4742008, rs301430, rs4742011, rs10120677, rs1890074, rs4448374 rs2891168, rs1333042, rs815845, rs2798062, rs1777052, rs6479594, rs290221, rs1755938, rs16936272, rs563, rs1130635, rs3812591, rs7919331, rs11254023, rs11254057, rs6602142, rs12218350, rs11008621, rs6481756 36, rs1907220, rs17663978, rs10901799, rs11016590, rs7113375, rs12291056, rs4755436, rs10838418, rs17788930, rs656104, rs17286033, rs2640785, rs1056136, rs582146, rs575848, rs11603786, rs2343877, rs1049376, rs2570 rs7980789, rs1729803, rs11178499, rs2137506, rs12580741, rs1526844, rs10861463, rs4293219, rs1009438, rs10773249, rs10847208, rs17083838, rs1322569, rs6563805, rs9532536, rs10492606, rs4942888, rs1413065, rs9564019rs rs179558, rs397080, rs761509, rs1450709, rs10133218, rs941714, rs941713, rs4417522, rs683922, rs1568679, rs8023369, rs4775035, rs11632583, rs1482929, rs922878, rs1482933, rs2672086, rs12441915, rs4338756, rs11633107 rs6497609, rs11649409, rs1424151, rs1056321, rs12451094, rs8070213, rs181535, rs9890602, rs7224525, rs12452064, rs199494, rs11079884, rs1003313, rs4476235, rs8075920, rs16976552, rs544748, rs891805, rs2269222, rs7234 r s1368456, rs2734456, rs2734454, rs2965106, rs6135852, rs8126205, rs6119304, rs6102788, rs2235862, rs4811206, rs3810550, rs6142738, rs2427293, rs735501, rs3003137, rs1543766, rs2839504, rs130414, rs4648462 rs9970705, rs11209252, rs1361493, rs7516969, rs1698598, rs1890303, rs6700410, rs41467544, rs7540764, rs11811532, rs17016333, rs6756667, rs11903594, rs2587693, rs7583123, rs17685109, rs2587693, rs7583123, rs17685106, rs896790, rs1441456, rs13007054 rs826431, rs4130090, rs844438, rs17069212, rs1502757, rs9871957, rs16849435, rs9815106, rs879394, rs6449420, rs16869447, rs2660341, rs6840349, rs17026134, rs2567372, rs41330746, rs3775851, rs1113890, rs8180155 rs6897211, rs409855, rs7705024, rs364211, rs13360374, rs7726729, rs17056003, rs7754459, rs4711718, rs7760603, rs1150093, rs4710773, rs4722237, rs788761, rs13311390, rs12718634, rs820935, rs10487463, rs3112341, rs4875712 s17652451, rs3739231, rs10974620, rs3780411, rs9632884, rs2798058, rs1777035, rs2798042, rs290226, rs573212, rs12339593, rs16929114, rs3124596, rs7099056, rs7922576, rs2688812, rs2688815, rs11199835, rs17104935 rs7120194, rs10502071, rs604411, rs7960985, rs4764380, rs863786, rs11177371, rs1512979, rs12230997, rs7990612, rs12018544, rs9315894, rs1588681, rs421841, rs9529485, rs8000868, rs179541, rs763388, rs10142332 rs12917583, rs3751664, rs757166, rs757164, rs16956781, rs10852333, rs12600303, rs4784219, rs12597838, rs9889056, rs6502937, rs4969326, rs1786809, rs11663052, rs16971109, rs17787324, rs4599012, rs12458940, rs7250638, rs12458940, rs7250638 rs1894469 and rs5764733,
A method to assist the diagnosis of the risk of developing open-angle glaucoma in the broad sense of the subject . The allele measurement process
A first SNP containing at least 30 SNPs including one or more SNPs selected from the core SNP group (first core SNP group) and a plurality of SNPs selected from the pool SNP group (first pool selection SNP group). The first measurement step to measure the group and
A total of at least 30 SNPs including one or more SNPs different from the first core SNP group (second core SNP group) and a plurality of SNPs selected from the pool SNP group (second pool selection SNP group) are included. This is a step including a second measurement step of measuring the second SNP group.
Here, the method according to claim 1, wherein the first pool selection SNP group and the second pool selection SNP group are not the same. The allele measurement process
A first SNP containing at least 30 SNPs including one or more SNPs selected from the core SNP group (first core SNP group) and a plurality of SNPs selected from the pool SNP group (first pool selection SNP group). The first measurement step to measure the group and
A total of at least 30 SNPs including one or more SNPs different from the first core SNP group (second core SNP group) and a plurality of SNPs selected from the pool SNP group (second pool selection SNP group) are included. The second measurement step for measuring the second SNP group, and
At least one or more SNPs (third core SNP group) different from the first core SNP group and the second core SNP group and a plurality of SNPs selected from the pool SNP group (third pool selection SNP group) are combined. It is a step including a third measurement step of measuring the third SNP group including 30 SNPs.
Here, the method according to claim 1, wherein the first pool selection SNP group, the second pool selection SNP group, and the third pool selection SNP group are all non-identical. The method according to any one of claims 1 to 3, wherein the information acquisition step is a step including a step of determining whether or not the measured allele is a risk allele and counting the total number of risk alleles. Biological samples are whole blood, whole blood cells, leukocytes, lymphocytes, plasma, serum, lymph, tears, saliva, nasal juice, cerebrospinal fluid, bone marrow, semen, sweat, mucosal tissue, skin tissue, or hair roots. The method according to any one of claims 1 to 4. The method according to any one of claims 1 to 5, wherein the cutoff value is a cutoff value previously determined by ROC analysis based on the SNP used in the allele measurement step. When the information providing step includes a plurality of measurement steps, the result of each measurement step regarding the subject obtained by the information acquisition step is based on the SNP group used in each measurement step. A process including a step of providing information on the high or low risk of developing broad-sense primary open-angle glaucoma by the subject as an index of whether or not the cutoff value is exceeded, which is determined in advance by ROC analysis. Item 2. The method according to any one of Items 2 to 5. Claimed claim, wherein the information providing step is a step of applying Bayes' theorem to the result obtained in the information acquisition step to calculate the probability that the subject has a risk of developing open-angle glaucoma in a broad sense. The method according to any one of 1 to 5. Probability that the information providing process applies the Bayes' theorem to the results obtained in the information acquisition process and has a probability of developing the broad-sense primary open-angle glaucoma of the subject with a probability of a preset percentage (%) or more. The method according to any one of claims 1 to 5, which is a step including a step of calculating. A computer comprising a processor and a memory under the control of the processor, said memory.
SNPs (pools) selected from a core SNP group consisting of 12 SNPs and a pool SNP group consisting of 471 SNPs based on single nucleotide polymorphism (SNP) allele information in biological samples collected from subjects. Allele measurement process, which measures alleles for at least 30 SNPs including the selected SNP group).
Based on the measurement results of the allele, the information acquisition process for acquiring information on the risk of developing open-angle glaucoma in the broad-sense subject, and based on the information obtained above, the broad-sense nuclear power plant of the subject. A subject at risk of developing open-angle glaucoma in a broad sense, in which a computer program for causing the computer to execute an information providing process that provides information for determining the risk of developing open-angle glaucoma is recorded. It ’s a detector ,
In the broad sense, if the information providing step exceeds the predetermined cutoff value based on the SNP used in the allergen measurement step for the result regarding the subject obtained by the information acquisition step, the subject is broadly defined. It is a step that includes a step of providing information that the risk of developing primary open-angle glaucoma is high, and if it is lower, the subject has a low risk of developing broad-sense primary open-angle glaucoma.
The 12 SNPs in the core SNP group are rs7623847, rs11159830, rs4852079, rs10853035, rs7531982, rs4430527, rs1399216, rs12437660, rs9442, rs1149332, rs11136906 and rs11956913.
471 SNPs in the pool SNP group are rs6429703, rs659046, rs11583644, rs835337, rs960501, rs3768184, rs6669702, rs2786755, rs1469876, rs2039153, rs12048011, rs9661521, rs7516960, rs515194, rs1127313, rs2661275, rs7524938 , Rs4233520, rs16850250, rs2841385, rs16855905, rs17313689, rs11680265, rs12615616, rs9332420, rs4344916, rs10490195, rs12713615, rs17030916, rs2587702, rs1529292, rs6747239, rs12477346, rs13408246, rs1627497, rs10804383 , Rs775779, rs775722, rs162871, rs1993802, rs1462793, rs4450855, rs9866028, rs7644682, rs1499787, rs9874964, rs7378503, rs7656362, rs956469, rs11945500, rs4295245, rs6814828, rs6837917, rs6847630, rs7664412, rs13 , Rs2036455, rs10011515, rs6835198, rs1392874, rs2567388, rs12641140, rs10049967, rs13118455, rs2675531, rs261133, rs261162, rs1501958, rs4701849, rs835136, rs865036, rs9688120, rs4354048, rs12659589, rs6883614 , Rs17097145, rs7744 710, rs197962, rs197963, rs2815019, rs4311505, rs11968166, rs1518516, rs4895745, rs7753862, rs1873329, rs9493858, rs9376182, rs646695, rs4394230, rs4535568, rs311330, rs311329, rs4870148, rs9480313, rs6908330 rs7788738, rs10282694, rs1123227, rs879965, rs17152102, rs6968827, rs6943901, rs1015573, rs12698976, rs10271370, rs2079162, rs10215082, rs1476446, rs6466117, rs2267889, rs6959243, rs4725651, rs3779853, rs6959243, rs4725651, rs3779853, rs4876223 rs11786580, rs17250119, rs2853236, rs13248227, rs729005, rs16929, rs273393, rs2221770, rs7009780, rs10815523, rs1331260, rs10815020, rs10974623, rs10974624, rs4742008, rs301430, rs4742011, rs10120677, rs1890074, rs4448374 rs2891168, rs1333042, rs815845, rs2798062, rs1777052, rs6479594, rs290221, rs1755938, rs16936272, rs563, rs1130635, rs3812591, rs7919331, rs11254023, rs11254057, rs6602142, rs12218350, rs11008621, rs6481756 36, rs1907220, rs17663978, rs10901799, rs11016590, rs7113375, rs12291056, rs4755436, rs10838418, rs17788930, rs656104, rs17286033, rs2640785, rs1056136, rs582146, rs575848, rs11603786, rs2343877, rs1049376, rs2570 rs7980789, rs1729803, rs11178499, rs2137506, rs12580741, rs1526844, rs10861463, rs4293219, rs1009438, rs10773249, rs10847208, rs17083838, rs1322569, rs6563805, rs9532536, rs10492606, rs4942888, rs1413065, rs10492606, rs4942888, rs1413065, rs9564019 rs179558, rs397080, rs761509, rs1450709, rs10133218, rs941714, rs941713, rs4417522, rs683922, rs1568679, rs8023369, rs4775035, rs11632583, rs1482929, rs922878, rs1482933, rs2672086, rs12441915, rs4338756, rs11633107 rs6497609, rs11649409, rs1424151, rs1056321, rs12451094, rs8070213, rs181535, rs9890602, rs7224525, rs12452064, rs199494, rs11079884, rs1003313, rs4476235, rs8075920, rs16976552, rs544748, rs891805, rs2269222, rs7234 r s1368456, rs2734456, rs2734454, rs2965106, rs6135852, rs8126205, rs6119304, rs6102788, rs2235862, rs4811206, rs3810550, rs6142738, rs2427293, rs735501, rs3003137, rs1543766, rs2839504, rs130414, rs4648462 rs9970705, rs11209252, rs1361493, rs7516969, rs1698598, rs1890303, rs6700410, rs41467544, rs7540764, rs11811532, rs17016333, rs6756667, rs11903594, rs2587693, rs7583123, rs17685109, rs2587693, rs7583123, rs17685106, rs896790, rs1441456, rs13007054 rs826431, rs4130090, rs844438, rs17069212, rs1502757, rs9871957, rs16849435, rs9815106, rs879394, rs6449420, rs16869447, rs2660341, rs6840349, rs17026134, rs2567372, rs41330746, rs3775851, rs1113890, rs8180155 rs6897211, rs409855, rs7705024, rs364211, rs13360374, rs7726729, rs17056003, rs7754459, rs4711718, rs7760603, rs1150093, rs4710773, rs4722237, rs788761, rs13311390, rs12718634, rs820935, rs10487463, rs3112341, rs4875712 s17652451, rs3739231, rs10974620, rs3780411, rs9632884, rs2798058, rs1777035, rs2798042, rs290226, rs573212, rs12339593, rs16929114, rs3124596, rs7099056, rs7922576, rs2688812, rs2688815, rs11199835, rs17104935 rs7120194, rs10502071, rs604411, rs7960985, rs4764380, rs863786, rs11177371, rs1512979, rs12230997, rs7990612, rs12018544, rs9315894, rs1588681, rs421841, rs9529485, rs8000868, rs179541, rs763388, rs10142332 rs12917583, rs3751664, rs757166, rs757164, rs16956781, rs10852333, rs12600303, rs4784219, rs12597838, rs9889056, rs6502937, rs4969326, rs1786809, rs11663052, rs16971109, rs17787324, rs4599012, rs12458940, rs7250638, rs12458940, rs7250638 rs1894469 and rs5764733,
A device for detecting subjects at risk of developing open-angle glaucoma in a broad sense . A computer program for a computer including a processor and a memory under the control of the processor to be executed .
SNPs (pools) selected from a core SNP group consisting of 12 SNPs and a pool SNP group consisting of 471 SNPs based on single nucleotide polymorphism (SNP) allele information in biological samples collected from subjects. Allele measurement process, which measures alleles for at least 30 SNPs including the selected SNP group).
Based on the measurement results of the allele, the information acquisition step of acquiring information on the risk of developing open-angle glaucoma in the broad sense of the subject, and based on the information obtained in the above, the broad sense of the subject A computer program that executes an information providing process that provides information for determining the risk of developing open-angle glaucoma.
In the broad sense, if the information providing step exceeds the predetermined cutoff value based on the SNP used in the allergen measurement step for the result regarding the subject obtained by the information acquisition step, the subject is broadly defined. It is a step that includes a step of providing information that the risk of developing primary open-angle glaucoma is high, and if it is lower, the subject has a low risk of developing broad-sense primary open-angle glaucoma.
The 12 SNPs in the core SNP group are rs7623847, rs11159830, rs4852079, rs10853035, rs7531982, rs4430527, rs1399216, rs12437660, rs9442, rs1149332, rs11136906 and rs11956913.
471 SNPs in the pool SNP group are rs6429703, rs659046, rs11583644, rs835337, rs960501, rs3768184, rs6669702, rs2786755, rs1469876, rs2039153, rs12048011, rs9661521, rs7516960, rs515194, rs1127313, rs2661275, rs7524938 , Rs4233520, rs16850250, rs2841385, rs16855905, rs17313689, rs11680265, rs12615616, rs9332420, rs4344916, rs10490195, rs12713615, rs17030916, rs2587702, rs1529292, rs6747239, rs12477346, rs13408246, rs1627497, rs10804383 , Rs775779, rs775722, rs162871, rs1993802, rs1462793, rs4450855, rs9866028, rs7644682, rs1499787, rs9874964, rs7378503, rs7656362, rs956469, rs11945500, rs4295245, rs6814828, rs6837917, rs6847630, rs7664412, rs13 , Rs2036455, rs10011515, rs6835198, rs1392874, rs2567388, rs12641140, rs10049967, rs13118455, rs2675531, rs261133, rs261162, rs1501958, rs4701849, rs835136, rs865036, rs9688120, rs4354048, rs12659589, rs6883614 , Rs17097145, rs7744 710, rs197962, rs197963, rs2815019, rs4311505, rs11968166, rs1518516, rs4895745, rs7753862, rs1873329, rs9493858, rs9376182, rs646695, rs4394230, rs4535568, rs311330, rs311329, rs4870148, rs9480313, rs6908330 rs7788738, rs10282694, rs1123227, rs879965, rs17152102, rs6968827, rs6943901, rs1015573, rs12698976, rs10271370, rs2079162, rs10215082, rs1476446, rs6466117, rs2267889, rs6959243, rs4725651, rs3779853, rs6959243, rs4725651, rs3779853, rs4876223 rs11786580, rs17250119, rs2853236, rs13248227, rs729005, rs16929, rs273393, rs2221770, rs7009780, rs10815523, rs1331260, rs10815020, rs10974623, rs10974624, rs4742008, rs301430, rs4742011, rs10120677, rs1890074, rs4448374 rs2891168, rs1333042, rs815845, rs2798062, rs1777052, rs6479594, rs290221, rs1755938, rs16936272, rs563, rs1130635, rs3812591, rs7919331, rs11254023, rs11254057, rs6602142, rs12218350, rs11008621, rs6481756 36, rs1907220, rs17663978, rs10901799, rs11016590, rs7113375, rs12291056, rs4755436, rs10838418, rs17788930, rs656104, rs17286033, rs2640785, rs1056136, rs582146, rs575848, rs11603786, rs2343877, rs1049376, rs2570 rs7980789, rs1729803, rs11178499, rs2137506, rs12580741, rs1526844, rs10861463, rs4293219, rs1009438, rs10773249, rs10847208, rs17083838, rs1322569, rs6563805, rs9532536, rs10492606, rs4942888, rs1413065, rs9564019rs rs179558, rs397080, rs761509, rs1450709, rs10133218, rs941714, rs941713, rs4417522, rs683922, rs1568679, rs8023369, rs4775035, rs11632583, rs1482929, rs922878, rs1482933, rs2672086, rs12441915, rs4338756, rs11633107 rs6497609, rs11649409, rs1424151, rs1056321, rs12451094, rs8070213, rs181535, rs9890602, rs7224525, rs12452064, rs199494, rs11079884, rs1003313, rs4476235, rs8075920, rs16976552, rs544748, rs891805, rs2269222, rs7234 r s1368456, rs2734456, rs2734454, rs2965106, rs6135852, rs8126205, rs6119304, rs6102788, rs2235862, rs4811206, rs3810550, rs6142738, rs2427293, rs735501, rs3003137, rs1543766, rs2839504, rs130414, rs4648462 rs9970705, rs11209252, rs1361493, rs7516969, rs1698598, rs1890303, rs6700410, rs41467544, rs7540764, rs11811532, rs17016333, rs6756667, rs11903594, rs2587693, rs7583123, rs17685109, rs2587693, rs7583123, rs17685106, rs896790, rs1441456, rs13007054 rs826431, rs4130090, rs844438, rs17069212, rs1502757, rs9871957, rs16849435, rs9815106, rs879394, rs6449420, rs16869447, rs2660341, rs6840349, rs17026134, rs2567372, rs41330746, rs3775851, rs1113890, rs8180155 rs6897211, rs409855, rs7705024, rs364211, rs13360374, rs7726729, rs17056003, rs7754459, rs4711718, rs7760603, rs1150093, rs4710773, rs4722237, rs788761, rs13311390, rs12718634, rs820935, rs10487463, rs3112341, rs4875712 s17652451, rs3739231, rs10974620, rs3780411, rs9632884, rs2798058, rs1777035, rs2798042, rs290226, rs573212, rs12339593, rs16929114, rs3124596, rs7099056, rs7922576, rs2688812, rs2688815, rs11199835, rs17104935 rs7120194, rs10502071, rs604411, rs7960985, rs4764380, rs863786, rs11177371, rs1512979, rs12230997, rs7990612, rs12018544, rs9315894, rs1588681, rs421841, rs9529485, rs8000868, rs179541, rs763388, rs10142332 rs12917583, rs3751664, rs757166, rs757164, rs16956781, rs10852333, rs12600303, rs4784219, rs12597838, rs9889056, rs6502937, rs4969326, rs1786809, rs11663052, rs16971109, rs17787324, rs4599012, rs12458940, rs7250638, rs12458940, rs7250638 rs1894469 and rs5764733,
Computer program .

Images